AI Governance and Compliance

Home >Courses >AI Governance and Compliance

Mentor Based

Program Syllabus

Module 1

About This Course

Participants will explore AI governance models, regulatory landscapes, and strategies for ensuring compliance with global standards such as GDPR, HIPAA, and emerging AI-specific laws. The program also delves into ethical decision-making, AI bias mitigation, and building transparent, accountable AI systems that align with legal and societal expectations.

Module 2

Aim

This program provides a comprehensive understanding of the frameworks, regulations, and best practices for governing AI technologies and ensuring compliance with ethical standards, data protection laws, and AI accountability.

Module 3

Workshop Objectives

Understand AI governance frameworks and their global implications.
Learn about compliance requirements for AI systems.
Explore ethical AI design and decision-making processes.
Develop strategies for bias mitigation and fairness in AI systems.
Build transparent, compliant, and accountable AI products.

Module 4

Modules for AI Governance and Compliance:

Introduction to AI Governance and Compliance
- What is AI Governance?
- The Importance of AI Governance and Compliance
- Key Regulatory Bodies and Standards (GDPR, CCPA, ISO, etc.)
Ethics in AI Development and Deployment
- Ethical Principles in AI (Fairness, Accountability, Transparency, and Ethics – FATE)
- Case Studies on AI Failures and Ethical Challenges
- Bias, Discrimination, and Fairness in AI Models
AI Compliance Frameworks and Regulations
- Overview of Global AI Regulations (GDPR, EU AI Act, CCPA)
- Key Components of an AI Compliance Framework
- Compliance Challenges in AI Model Development
Risk Management for AI Systems
- Identifying and Mitigating Risks in AI Development and Deployment
- Managing Data Privacy and Security in AI Systems
- Tools and Techniques for Risk Assessment and Mitigation
Bias and Fairness in AI Models
- Understanding and Identifying Bias in AI Algorithms
- Techniques for Auditing AI Models for Fairness
- Best Practices for Fairness in Data Collection, Model Training, and Deployment
Transparency and Explainability in AI
- The Importance of Explainable AI (XAI)
- Tools and Techniques for Making AI Models Interpretable
- Regulatory Requirements for AI Transparency and Explainability
AI Accountability and Responsibility
- Assigning Responsibility in AI Development
- AI Decision-Making: Human-in-the-Loop vs. Fully Automated Systems
- Accountability Frameworks for AI Systems
Privacy and Data Protection in AI
- Understanding Data Privacy Regulations (GDPR, HIPAA, etc.)
- Techniques for Privacy-Preserving AI (Differential Privacy, Federated Learning)
- Managing Personally Identifiable Information (PII) in AI Systems
Auditing and Monitoring AI Systems
- AI Model Auditing Practices
- Continuous Monitoring of AI Models for Compliance
- AI Model Lifecycle Management and Updates for Compliance
AI Governance in Practice
- Building Governance Teams and AI Ethics Boards
- Best Practices for AI Governance Implementation in Organizations
- Creating AI Policies and Guidelines for Businesses
Regulatory Challenges and Future of AI Governance
- Emerging Trends in AI Regulation
- Navigating Regulatory Changes and Preparing for the Future
- Case Studies on AI Regulatory Compliance

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.